Renewable power generation, and wind power in particular, fluctuates due to natural variation in the energy source. For example, atmospheric turbulence has been known to fluctuate as well as the wind itself sharing spectral features of the turbulent wind from which it derives energy. Regarding wind specifically, whereas distributed wind farms are intended to smooth these fluctuations, power entering the grid that is generated from wind still fluctuates wildly which poses a risk of increased grid instability and corresponding reserve costs.
To manage these fluctuations, utility operators may use forecast models to predict these fluctuations over a future time horizon (e.g., few hours to 1 day). This is done to account for operating reserves known as standby power needed for demand not met by the fluctuating renewable energy sources. This is also done to protect grid infrastructure from instability and black-out risk owing to sudden surges in power from strong fluctuations in the renewable energy source.
Forecast models inherently are never perfectly accurate and the degree of robustness to be incorporated in to smart-grids depends upon accuracy of forecast error and how this error varies over time. In turn, knowledge of this accuracy helps determine response time requirements when designing the modern, smart-grid.
Accordingly, a solution that imparts a reliable manner to capture and/or mitigate the accuracy of forecast error as well as the time variation of error may be needed. It is with respect to these and other considerations that the various embodiments described below are presented.